Xuan Zhou1,*, Jianping Yi2
Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2103-2116, 2023, DOI:10.32604/iasc.2023.034855
Abstract Mining more discriminative temporal features to enrich temporal context representation is considered the key to fine-grained action recognition. Previous action recognition methods utilize a fixed spatiotemporal window to learn local video representation. However, these methods failed to capture complex motion patterns due to their limited receptive field. To solve the above problems, this paper proposes a lightweight Temporal Pyramid Excitation (TPE) module to capture the short, medium, and long-term temporal context. In this method, Temporal Pyramid (TP) module can effectively expand the temporal receptive field of the network by using the multi-temporal kernel decomposition without significantly increasing the computational cost.… More >
Login
Register
